About the job
About Us:
At impact.com, we are dedicated to our people and technology, with a relentless focus on customer success. We thrive on collaboration, which fuels our rapid growth and ability to serve some of the world's largest brands. Utilizing cutting-edge technology, we tackle real-world challenges for our clients and lead the industry as the foremost SaaS platform for automating partnerships and maximizing revenue. Our entrepreneurial spirit fosters a culture that rewards ambition and curiosity. If you seek a team that values your input, recognizes your contributions, and offers a vibrant environment filled with talented individuals from around the globe, look no further!
As the leading partnership management platform globally, impact.com is revolutionizing how businesses manage and enhance their partnerships, from traditional rewards affiliates to influencers, content publishers, and more. Our robust, purpose-built platform simplifies the creation, management, and scaling of partnership ecosystems that customers trust for their purchasing, information, and entertainment needs. To discover how impact.com’s technology and marketplace are propelling revenue growth for renowned global brands like Walmart, Uber, Shopify, and L’Oreal, visit www.impact.com.
Your Role:
As the Team Lead for Analytics Data Platform at impact.com, you will be responsible for overseeing the architecture, reliability, and progression of our Analytics Data Platform, which processes terabyte-scale data for our leading partnership SaaS platform. You will provide hands-on technical leadership across various platform subsystems, design scalable infrastructure, spearhead strategic initiatives, establish engineering standards, and mentor engineers at different levels. This role focuses on system and platform engineering with an emphasis on distributed data processing at terabyte scale. You will engage with the complete data ecosystem, including ingestion, processing, storage, orchestration, access, and governance, leveraging technologies such as Scala, Python, Google Cloud Dataproc, Databricks, BigQuery, and Airflow. The ideal candidate possesses profound expertise in distributed systems and the capability to lead architectural decisions across teams, translating platform trade-offs into actionable insights.
